Results (PhD Chapter 2)
Section 1/2
Results (PhD Chapter 2)
This series of files compile all analyses done during Chapter 2:
- Section 1 presents indices of influence calculations.
- Section 2 presents HMSC and regressions results.
All analyses have been done with R 3.6.0.
Click on the table of contents in the left margin to assess a specific analysis
Click on a figure to zoom it
To assess Section 2, click here.
To go back to the summary page, click here.
Human activities considered for the analyses:
- city influence: CityInf
- industries influence: InduInf
- dredging collecting zones: DredColl
- dredging dumping zones: DredDump
- commercial ships mooring site: MoorSite
- rainwater sewers: RainSew
- wastewater sewers: WastSew
- city wharves: CityWha
- industries wharves: InduWha
- fisheries (gear used):
- traps: FishTrap
- bottom-trawling: FishTraw
- longline: FishLine
- nets: FishNet
- dredge: FishDred
Data is also available for the number of captured individuals for dogwhelk (Buccinum sp.), common crab (Cancer irroratus), snowcrab (Chinoecetes opilio), nordic shrimp (Pandalus borealis), arctic surfclam (Mactromeris polynyma) and american lobster (Homarus americanus) fisheries.
Workspace preparation
library(marmap)
library(pander)
library(raster)
library(reshape2)
library(sf)
library(stats)
library(tidyverse)1. Maps
Depth
Isobaths
2. Modelling of non-fisheries human activities influence (WIP)
The influence of each human activity has been modelled at each station, in order to be later used in prediction models (see section 2).
We calculated an index of influence for each activity \(I_{ij}\). The weighting parameter \(w_{j}\) is specific for each activity.
\[ I_{ij} = w_{j} . P_{ij} \]
For non-fisheries activities, the probability of influence \(P_{ij}\) has been calculated based on the distance from the source(s), bathymetry and hydrodynamics influences.
\[ P_{ij} = f_{j} \left( D_{ij}, \Delta Z_{ij}, H_{i} \right) \]
- \(i\) is a station
- \(j\) is a human activity
- \(f_{j}\) is the decay function of activity \(j\)
- \(D_{ij}\) is the distance of station \(i\) from the source of activity \(j\)
- \(\Delta Z_{ij}\) is the bathymetry index at station \(i\) for activity \(j\)
- \(H_{i}\) is the hydrodynamic index at station \(i\)
STEP 1: Distance form the source and decay function
This corresponds to \(D_{ij}\) and \(f_{j}\) in Formula 2.
First, we need to calculate the distance of each station from the source(s) of the activity. This proxy will allow to take coasts and island into consideration (necessary at BSI), and will then be used to calculated the probabilities of influence. Distances are in meters and were computed with the package gdistance.
Second, each human activity has its own decay function \(f_{j}\). A careful literature review will be needed to support it, as parameters and mathematical links will be decided and settled by us. The help of Frédéric Guichard may be needed! A parameter max_inf needs to be defined for each activity, beyond which the effect of an AH can be neglected.
The following maps present the values of \(D_{ij}\) before and after the use of the decay function \(f_{j}\). The latter will be used for the following steps.
CityInf
Raw distances
With decay function
A linear decay function is used here, with parameter max_inf set to 4500.
InduInf
Raw distances
With decay function
A linear decay function is used here, with parameter max_inf set to 4500.
DredColl
Raw distances
With decay function
A linear decay function is used here, with parameter max_inf set to 1000.
DredDump
Raw distances
With decay function
A linear decay function is used here, with parameter max_inf set to 1000.
MoorSite
Raw distances
With decay function
A linear decay function is used here, with parameter max_inf set to 3000.
RainSew
Raw distances
With decay function
A linear decay function is used here, with parameter max_inf set to 3000.
WastSew
Raw distances
With decay function
A linear decay function is used here, with parameter max_inf set to 3000.
CityWha
Raw distances
With decay function
A linear decay function is used here, with parameter max_inf set to 3000.
InduWha
Raw distances
With decay function
A linear decay function is used here, with parameter max_inf set to 3000.
STEP 4: Probability of influence
This corresponds to \(P_{ij}\) in Formulae 1 and 2.
Here, we will combine each component obtained in the previous steps to calculate \(P_{ij}\).
TO BE ADDED.
CityInf
InduInf
DredColl
DredDump
MoorSite
RainSew
WastSew
CityWha
InduWha
STEP 5: Weighting parameter
This corresponds to \(w_{j}\) in Formula 1.
The following table shows the weights \(w_{j}\) for each non-fishery human activity:
| activity | weights |
|---|---|
| CityInf | 1 |
| InduInf | 1 |
| DredColl | 1 |
| DredDump | 1 |
| MoorSite | 1 |
| RainSew | 1 |
| WastSew | 1 |
| CityWha | 1 |
| InduWha | 1 |
STEP 6: Index of influence
This corresponds to \(I_{ij}\) in Formula 1.
Finally, we combine \(P_{ij}\) and \(w_{j}\) in order to calculate \(I_{ij}\).
TO BE ADDED.
CityInf
InduInf
DredDump
MoorSite
RainSew
WastSew
CityWha
InduWha
3. Modelling of fisheries human activities influence (WIP)
The influence of each human activity has been modelled at each station, in order to be later used in prediction models (see section 2).
We calculated an index of influence for each activity \(I_{ij}\). The weighting parameter \(w_{j}\) is specific for each activity.
\[ I_{ij} = w_{j} . P_{ij} \]
\(P_{ij}\) for fisheries activities have been calculated thanks to David Beauchesne’s Saint-Lawrence Database, according to the intensity of the gears deployed.
STEP 2: Weighting parameter
This corresponds to \(w_{j}\) in Formula 3.
The following table shows the weights \(w_{j}\) for each fishery:
Quitting from lines 475-476 (C2_analyses_1.Rmd) Error in pandoc.table.return(…) : Wrong number of parameters (2 instead of 3) passed: justify
STEP 3: Index of influence
This corresponds to \(I_{ij}\) in Formula 3.
We can then combine \(P_{ij}\) and \(w_{j}\) to calculate \(I_{ij}\).
TO BE ADDED.